• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.7-14
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• 2006

Most researches about rumble strips have Presented only the before-and-after analysis of the accidents. So, Researchers have not dealt with the estimation of rumble strip's effectiveness on the driver's alertness. In this study. the effectiveness of the rumble strips on the driver's alertness was estimated by measuring the bio-signal transmitted from the driver. The bio-signal acquired for this experiments were theta wave in central lobe. The experimental results revealed that the theta waves as measured form the drivers's head while in the rumble strip section differed from those while in non-rumbled section; 74 percent decrease in theta wave value, respectively. This fact finding could mean that the driver's alertness increased from 74 percent while in the rumble strip section of the road. In all five trials of driving experiments on the rumble strip section, all the drivers showed the best alertness as measured by the theta waves in the first driving trial.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.43-51
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• 2009

This paper investigates the accuracy of vehicle pre-braking speed estimates based upon tire/roadway coefficient of friction (drag factor) measurements and skid mark measurements Data for pre-braking and pre-skidding speeds were collected to determine if there were any correlations between pre-braking speeds and pre-skidding speeds. Braking tests were performed on two vehicles using various measurement devices including a fifth wheel, a speed gun, and a vericom 2000. The vehicle speeds, braking distances, skid mark distances, and deceleration histories were recorded. From these data. coefficients of friction and vehicle pre-skidding speeds for the tested road surface were calculated. The pre-skidding speeds were then compared to the actual pre-braking speeds of the vehicles in order to establish relationships between pre-skidding and pre-braking speed. A correlation between the Pre-skidding speed and the actual pre-braking speed was established for the study data.

• 한국지형공간정보학회:학술대회논문집
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• 2004.10a
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• pp.25-31
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• 2004

To obtain the 3-D coordinates of the urban roads from aerial images, the accurate matching technique in road areas is required. In this paper, we suggest the relational matching method that is performed by comparison of relationships of road pavement markings after they are extracted from aerial images using geometric properties and spatial relationships of the pavement markings. Relational matching requires not only high level description of features but also the solution for inexact matching problems. In addition, it needs a lot of tests for the reliable final result. In this research, we described features as calculating geometric properties of the pavement markings, suggested the solution for inextact matching problems, and performed tests to decide whether the result is acceptable or not, which use the property that road areas are flat. In order to evaluate the accuracy of matching, we made a visual evaluation and compared the result of this technique with those measured by analytical photogrammetry.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.5
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• pp.717-732
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• 2017

Recently, the construction of underground structure such as a double-deck tunnel is increasing to manage rapid growth of roadway traffic volume. Double-deck tunnel includes middle slab to separate upper and lower road inside, and various sources affect the dynamic behaviour of middle slab due to dynamic loading of vehicle. Therefore, it is important to investigate the dynamic response of middle slab precisely to apply it to design and analysis of double-deck tunnel. In this study, dynamic analysis model of middle slab considering structural type, design velocity, vehicle load, and surface roughness, etc. is built. 3-dimensional dynamic analysis is performed to assess dynamic response of middle slab. Consequently, Dynamic Magnification Factor which represents dynamic response of middle slab shows maximum in case of elastomeric bearings (EB) and average roughness (Grade C). It is also expected that dynamic response can be reduced under the condition of good roughness (Grade B) and fixed bearings (FB).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.71-79
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• 2019

By using convolutional neural networks (CNNs) based on semantic segmentation, road surface damage detection has being studied. In order to generate the CNN model, it is essential to collect the input and the corresponding labeled images. Unfortunately, such collecting pairs of the dataset requires a great deal of time and costs. In this paper, we proposed a road surface damage detection technique based on semi-supervised learning using pseudo labels to mitigate such problem. The model is updated by properly mixing labeled and unlabeled datasets, and compares the performance against existing model using only labeled dataset. As a subjective result, it was confirmed that the recall was slightly degraded, but the precision was considerably improved. In addition, the $F_1-score$ was also evaluated as a high value.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.106-118
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• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.68-68
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• 2023

호우로 인한 도시 지역의 침수는 빗물받이의 능력 부족, 우수관로 용량 부족 등 다양한 요인에 의하여 발생한다. 구체적으로 빗물받이 능력 부족으로 인한 침수는 노면에 떨어진 우수가 빗물받이로 유입하지 못하고 지표면을 유하하여 저지대에 빗물이 모여 발생하게 되며 우수관로의 용량부족은 설계 강우 이상의 호우가 발생하거나 하류 배수위 영향으로 인하여 우수관로 내 만관이 발생하여 맨홀 월류로 인하여 침수가 발생하게 된다. 도시지역의 침수를 경감하기 위해서는 구조적, 비구조적인 대책을 마련할 수 있으며 본 연구에서는 국부적인 노면 침수가 발생하는 주요 도로 주변부의 침수발생을 효율적으로 제어하기 위한 구조적 대책 중 하나인 UHPC 저류침투시설 도입에 따른 홍수저감 효과를 수리수문학적 분석하는 방법을 개발하였다. 분석 절차는 ① 대상지역 현황 분석을 위한 관망도, 수치지형도, 토지피복도, 토양도 등 관련 자료의 조사 ② 침수 발생 현황 및 시설물 설치에 따른 효과분석을 위한 강우분석 ③ 주요 호우 시 대상지역 침수발생 현황 분석 ④ 저수지 홍수추적 기법을 활용한 저류침투시설 도입효과 계산 ⑤ 주요 호우별 홍수조절효과 분석결과를 기초로 저류침투시설 도입에 따른 침수 저감 효과를 2차원으로 검토의 5개 단계로 이루어진다. 노면을 따라서 유하 또는 맨홀 월류에 의해 저류 침투시설로 유입하는 우수의 홍수조절 효과 분석은 연구에서 개발한 스프레드시트 기반 저수지 홍수추적 기법을 활용하여 분석하였으며 강우-유출량 중 10 %가 시설물로 유입하는 가정 조건하에 시설물로 유입하는 우수의 저류-침투-방류를 동시에 고려하여 계산하였다. 다만, 저류침투시설로 유입하는 노면 유출수의 정량적 수치는 자연적 요인 및 다양한 제약 조건에 따라 큰 차이가 있을 수 있으며 불확실성이 높으므로 노면 유출수의 저류침투시설 유입량의 추가적인 연구가 필요하다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1A
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• pp.39-49
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• 2008

Dynamic responses of steel plate girder bridges considering road surface roughness of bridge and bridge-vehicle interaction are investigated by numerical analysis. Simply supported steel plate girder bridges with span length of 20 m, 30 m, and 40 m from "The Standardized Design of Highway Bridge Superstructure" published by the Korean Ministry of Construction are used for bridge model and the road surface roughness of bridge decks are generated from power spectral density(PSD) function for different road. Three different vehicles of 2- and 3-axle dump trucks, and 5-axle tractor-trailer(DB-24), are modeled three dimensionally. For the bridge superstructure, beam elements for the main girder, shell elements for concrete deck, and rigid links between main girder and concrete deck are used. Impact factor and DLA of steel plate girder bridges for different spans, type of vehicles and road surface roughnesses are calculated by the proposed numerical analysis model and compared with those specified by several bridge design codes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.815-825
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• 2017

Visibility of lane marking is impaired at night or in the rain, which thereby threatens traffic safety. Recently, various studies and technologies have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment productivity, improvement of lane marking visibility by applying phosphorescent material mixed paint. Cost-benefit analysis was performed with considering various benefit items that can be expected. About 45% of traffic accidents would be prevented by improving lane marking visibility. Additionally, accident reduction benefit and traffic congestion reduction benefit were calculated as much as 246 billion KRW per year and 12 billion KRW per year, respectively, by reducing repaint cycle due to enhanced durability. 45 billion KRW per year is expected to reduced with improved lane detection performance of autonomous vehicle. Meanwhile, total increased cost when introducing phosphorescent material mixed paint to 91,195km of nationwide road is identified as 1922 billion KRW per year. However, economic feasibility could not be secured with 0.16 of cost-benefit ratio when applied to the road network as a whole. In case of "Accident Hot Spot" analyzing section window (400m), one or more fatality or two or more injured (one or more injured in case of less than 2 lanes per direction) per year were caused by pavement marking related accident, economic feasibility was secured. In detail, 3.91 of cost-benefit ratio is estimated with comparison of the installation cost for 5,697 of accident hot spot and accident reduction benefit. Some limitations and future research agenda have also been discussed.